Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

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Author(s)
Chen, X
Zhang, L
Gippel, CJ
Shan, L
Chen, S
Yang, W
Griffith University Author(s)
Year published
2016
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Show full item recordAbstract
Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor
performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based
measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method
will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to
assimilate radar rainfall data transformed from the classified 𝑍-𝑅 relationship, and the postassimilation data were compared ...
View more >Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified 𝑍-𝑅 relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methodswere superior.Thecorresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.
View less >
View more >Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified 𝑍-𝑅 relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methodswere superior.Thecorresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.
View less >
Journal Title
Advances in Meteorology
Copyright Statement
© 2016 Xinchi Chen et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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This publication has been entered into Griffith Research Online as an Advanced Online Version.
Subject
Atmospheric sciences
Atmospheric sciences not elsewhere classified